Monday, October 16, 2006

Maximizing growth without risking bankruptcy

Many hedge fund disasters come not from making the wrong bets – that happen to the best of us – but from making too big a bet by overleveraging. On the other hand, without using leverage (i.e. borrowing on margin to buy stocks), we often cannot realize the full growth potential of our investment strategy. So how much leverage should you use?

Surprisingly, the answer is well-known, but little practiced. It is called the Kelly criterion, named after a mathematician at Bell Labs. The leverage f is defined as the ratio of the size of your portfolio to your equity. Kelly criterion says: f should equal the expected excess return of the strategy divided by the expected variance of the excess return, or

f = (m-r)/s2

(The excess return being the return m minus the risk-free rate r.)

This quantity f looks like the familiar Sharpe ratio, but it is not, since the denominator is s2, not s as in the Sharpe ratio. However, if you can estimate the Sharpe ratio, say, from some backtest results of a strategy, you can also estimate f just as easily. Suppose I have a strategy with expected return of 12% over a period with risk-free rate being 4%. Also, let’s say the expected Sharpe ratio is 1. It is easy to calculate f, which comes out to be 12.5.

This is a shocking number. This is telling you that for this strategy, you should be leveraging your equity 12.5 times! If you have $100,000 in cash to invest, and if you really believe the expected values of your returns and Sharpe ratio, you should borrow money to trade a $1.2 million portfolio!

Of course, estimates of expected returns and Sharpe ratio are notoriously over-optimistic, what with the inevitable data-snooping bias and other usual pitfalls in backtesting strategies. The common recommendation is that you should halve your expected returns estimated from backtests when calculating f. This is often called the half-Kelly criterion. Still, in our example, the recommended leverage comes to 6.25 after halving the expected returns.

Fixing the leverage of a portfolio is not as easy or intuitive as it sounds. Back to our $100,000 example. Say you followed the (half-) Kelly criterion and bought a portfolio worth $625,000 with some borrowed money. The next day, disaster struck, and you lost 5%, or $31,250, of the value of your portfolio. So now your portfolio is worth only $593,750, and your equity is now only $68,750. What should you do? Most people I know will just stick to their guns and do nothing, hoping that the strategy will “recover”. But that’s not what the Kelly criterion would prescribe. Kelly says, if you want to avoid eventual bankruptcy (i.e. your equity going to zero or negative), you should immediately further reduce the size of your portfolio to $429,688. Why? Because the recommended leverage, 6.25, times your current equity, $68,750, is about $429,688.

Thus Kelly criterion requires you to sell into a loss (assuming you have a long-only portfolio here), and buys into a profit – something that requires steely discipline to achieve. It also runs counter to the usual mean-reversion expectation. But even if you strongly believe in mean-reversion, as no doubt many of the ruined hedge funds did, you need to consider protecting you and your investors from the possibility of bankruptcy before the market reverts.

Besides helping you to avoid bankruptcy, the Kelly criterion has another important mathematically proven property: it is a “growth-optimal” strategy. I.e. if your goal is to maximize your wealth (which equals your initial equity times the maximum growth rate possible using your strategy), Kelly criterion is the way.

Notice this goal is not the same as many hedge managers’ or their investors’ goal. They often want to maximize their Sharpe ratio, not growth rate, for the reason that their investors want to be able to redeem their shares at any time and be reasonably sure that they will redeem at a profit. Kelly criterion is not for such investors. If you adopt the Kelly criterion, there may be long periods of drawdown, highly volatile returns, low Sharpe ratio, and so forth. The only thing that Kelly guarantees (to an exponentially high degree of certainty), is that you will maximize the growth potential of your strategy in the long run, and you will not be bankrupt in the interim because of the inevitable short-term market fluctuations.

64 comments:

Anonymous
said...

"This is telling you that for this strategy, you should be leveraging your equity 12.5 times! If you have $100,000 in cash to invest, and if you really believe the expected values of your returns and Sharpe ratio, you should borrow money to trade a $1.2 million portfolio!"

Hi, I'm not sure this is the correct interpretation of the Kelly Criterion... if f is 8% (the reciprocal of 12.5), and the trader's net worth is $100k, shouldn't he only trade a portfolio of $8k? Ie. if he has $8k in cash, he should buy $8k worth of stock but not borrow anything; if he has no cash (eg. $100k vested in properties), then he should borrow $8k to buy stock. In other words, one should only trade a fraction of one's net worth, and not borrow if he has enough cash on hand to take the trade. Or am I missing something here?

You should not be taking the reciprocal of f=12.5. f itself is the fraction of your wealth you should use for betting. This is intuitively clear since f is proportional to the excess returns of your strategy -- the higher the return, the higher the fraction. If the "fraction" turns out to be greater than 1, that means you should bet more than your net worth, i.e. use leverage.

Is the Kelly fraction (f) in your formula the same f as in http://en.wikipedia.org/wiki/Kelly_criterion (f=p-q/b)? On the wiki page, the maximum for p is 100% and the minimum for q is 0%, hence f can never exceed unity and that is why I thought f was 8% instead of 12.5. This could be the cause of the confusion here...

The Kelly's formula you quoted is for betting with binary outcomes only. In the case of continuous finance, please refer to the formula I wrote in the main body of my article which comes from the paper by Prof. Ed Thorpe cited at the end of that article. In the continuous finance case, f can most definitely be greater than 1, otherwise it would be quite useless to the hedge fund community.Ernie

This works, like much of financial theory, only in the case of continuous hedging without trading costs. In the real world, different from the lognormal world, there is a real possibility of bankruptcy because one is not able to hedge continuously and because firms can go bankrupt--geometric returns don't work for the possibility of a 100% loss. The optimal leverage with limited hedging (say once per month) and trading costs should be substantially smaller than the make-believe world of the Kelly criterion which holds for gambling (where one can adjust the wager before each bet and returns don't have fat tails) but not for real financial markets. Otherwise one might well run into the surprise of a "seven-sigma" event encountered by many a hedge fund.

I disagree with your statement that financial theory works only when there is no transaction cost or when we assume log-normality. In fact, most of the papers published in finance these days explicitly incorporate transaction costs in their modeling. Furthermore, if you read Dr. Ed Thorpe's paper on Kelly criterion, you will find that he has managed a well-known and highly successful hedge fund using this technique. On the other hand, many hedge fund managers that "blew up" have never heard of Kelly's formula.

As quantitative practitioners, just as if we were physicists or biologists, we are of course aware that every theory is an approximation, but that does not mean that theories are useless for practical implementation.

Ernie: I didn't mean to say that nothing of financial theory works in practice, and I follow Dr. Thorpe religiously. My point merely was that--like unmodified Black-Scholes which rests on similar assumptions--applying the Kelly criterion to a lognormal random walk seriously underestimates risk and thus overestimates optimal leverage. The basic idea of the Kelly criterion, optimize geometric return of your portfolio, makes a lot of sense, only the optimal leverage will be substantially lower than a naive application of the Kelly criterion suggests. You can convince yourself of this if you do a Monte-Carlo simulation of differently leveraged portfolios with actual return distribution instead of Gaussian returns, periodic instead of continuous rehedging, and trading costs.

You are right in saying that a log-normal model of risk underestimates the true risk of a portfolio. That's why most practitioners use the half-Kelly formula to increase the margin of safety. I would regard the leverage that Kelly formula suggests (with log-normal assumptions) as an upper limit.

Another interesting observation: If you believe that you don't have any special portfolio management skills and for the invested asset mu = rf + rp*sigma then the optimal leverage under GBM etc. works out to f=rp/sigma, i.e. the market price of risk divided by your investment's volatility.

The half in half-Kelly comes from the cumulative experience of traders using Kelly's formula to manage their investments. The consensus opinion is that taking half of the Kelly number gives a good enough safety margin.

I am not familiar with the acronym "GBM". Perhaps you can elaborate or give a reference to that formula?

Hi Ernie: There are several anonymous posters active now, which creates some confusion. I wasn't the one with the half-Kelly question, though I'd probably do some modelling under the more realistic assumptions I outlined to gain confidence before trading it. Anyhow, with GBM I meant geometric Brownian motion--I just put that in as a caveat because earlier I said that this model underestimates risk.

Quant traders rely on backtesting, not just their own, but other traders collective back (and live) testing. Most strategies do not have proofs in the mathematical sense. And yes, I regret to inform you that a majority of quant traders (based on my circle of acquaintances) do rely on gut instincts in a number of places, and the "1/2" in half-Kelly is one of those places. Since you follow Dr. Thorpe's work religiously, you will notice he use half-Kelly in his paper too, where he also did not give any "proof" that 1/2 is the best fraction to use. However, there are numerous simulations that show that, reducing leverage to half reduces the chance of ruin greatly without reducing the growth rate of wealth significant.

There's the trader's backtested strategy and there's the "ideal" leverage for that given strategy.

GIVEN a strategy,u assert that the ideal leverage is 1/2 kelly.If so,this question can be treated mathematically (or via monte carlo) given the outcome of that strategy; either simplistically by a wager and % success rate, or by your mentioned continued "finance case" stream of possible outcomes.

Indeed,a strategy does not have any proof,but the leverage for a particular strategy does.

I have not read any papers or simulations defending the use of 1/2 kelly and would love to read them. Please post some links to them.Congrats on your great thread and initiative.

1/2 is arbitrary. In the spirit of much Bayesian hackery (e.g. shrink all off-diagonal covariances 50% towards the mean...), half seems to be a happy medium :-)

The reason full kelly betting isn't used is not because it's "wrong" (i.e. lognormal vs. fat tailed, though that causes problems), but because we usually don't really know the expected return nor variance. We might be able to make good guesses, but they are still guesses. Kelly shows that overbetting leads to ruin, whereas underbetting is "merely" suboptimal.

Numbers such as returns and volatility are all estimates. The exact leverage is indeed guesswork. What's important is that one must adjust the portfolio size in order to maintain a fairly constant leverage. (And even this is sometimes quite hard if you don't know ahead of time how many positions you are going to have.)

Given the probabilistic nature of returns, it is always possible, for a given historical returns series, to construct a "moving" leverage that generates higher total growth than Kelly's formula.

In fact, you can always set the leverage to zero just before a down day, and set it to infinity just before an up day!

So I am not exactly clear what your "moving leverage" strategy is.

What Kelly has proven mathematically, however, is that if your returns time series is stationary, in the sense of stationary returns, volatility, and therefore Sharpe ratio, then statistically the leverage given by Kelly's formula will generate the maximum growth rate. It is impossible to exceed this leverage and not risk ruin (i.e. equity goes to zero). However, as another reader pointed out before, due to the uncertainty in estimates of return and volatility, it is advisable to be on the safe side and halve the leverage.

Increasing leverage as gains accumulate is, according to Kelly's theorem, very likely to lead to ruin, unless you can justify an increase in expected Sharpe ratio going forward as well. Now, this is different from increasing the bet (capital) size as gains accumulate. We should of course engage in the latter without changing the leverage at all.

The Kelly criterion is based on the assumption that you can borrow at the risk-free rate. In your example, if you borrow at 4% over the risk-free rate, then the optimal leverage works out to be 6.25 (half-Kelly in this case).

See the following post where I discuss the effect of higher borrowing rates: link

However, I don't know where you can find an investment that has a 12% return with a standard deviation of only 8%. If the standard deviation is 20% (roughly matching long-term volatility of the S&P 500), then the Sharpe ratio drops to 0.4, and the Kelly criterion drops to f=2. Toss in the assumption of borrowing at 4% above the risk-free rate, and the optimal leverage point drops to f=1 (i.e., no borrowing at all).

It's amazing how the case for leverage evaporates as you repair unrealistic assumptions.

Dear Michael,You are right that if your funding cost increases, the Kelly leverage will be reduced. However, your assumption of 4% over risk-free rate is far too onerous. For e.g., if you trade through Interactive Brokers (popular choice for many independent traders and small hedge funds), the funding rate is only 1% or less above risk-free rate.

You are also correct in pointing out that if we own S&P500 index, we will not get returns of 12% vs. standard deviation of 8% (a Sharpe ratio of 1.5). However, many market-neutral strategies that quantitative traders employ have even better returns vs. risk ratio than what I used as an example here. In fact, Sharpe ratios (after cost) of over 2 are quite common. So I don't believe these are unrealistic assumptions -- and certainly high leverage is very appropriate for such strategies.

I can't say that I know much about brokers other than the one I use. I can borrow only a modest amount from my broker in the form of margin. After that, I woud need to get a loan elsewhere at higher rates. It is unlikely that I could borrow even 3 times the size of my portfolio at any interest rate.

Of course, hedge funds are very likely to have better access to borrowed funds than I have. However, given the number of hedge funds that go under, I'm guessing that many of them assess the volatility of their strategies incorrectly.

Suppose that a hedge fund has a strategy with expected excess return 20% and standard deviation of 10% (Sharpe Ratio 2). Borrowing at 1% excess leads to f=19. If such a fund had $10 million in assets and sought to borrow $180 million at 1% excess, the potential lender would be insane (in my opinion) to agree. The strategy seems too good to be true. The expected leveraged geometric return is 181.5%. But this drops to -44% if the standard deviation is actually 15% instead of 10%.

If crazy lenders exist out there then I can't blame hedge funds for trying their strategies. It amounts to a huge free roll. If things work out, then everybody gets rich. If it all goes south, then the lender loses a pile of money.

Michael,If you have a dollar or market neutral portfolio, you can borrow from some brokers (such as Interactive Brokers) at higher than Reg-T margin. It is called risk-based margining or portfolio margining.

The Kelly formula I have displayed here is based on a Gaussian model of returns, which is clearly wrong in cases of extreme market events. That's why half-Kelly is the more prudent choice.

(As a commercial pitch for my forthcoming book, I have a long and detailed chapter on the application of Kelly formula to real-life trading.)

Hey Ernie, Based on what I have read so far, Kelly Crtn talks about optimal bet size based on the key variable of probability of success, while optimal leverage is very different concept, which talks about how much to leverage based on portfolio/equity expected returns/variace. Please clarify the reason why for you to combine Kelly's concept with Optimal leverage?

Hi Vibhu,Your question has been discussed elsewhere on this blog and in the comments. The quick answer is that I am using the continuous finance version of Kelly formula. See http://www.edwardothorp.com/sitebuildercontent/sitebuilderfiles/KellyCriterion2007.pdf

Dear ErnieI backtested the GLD-GDX strategy for the last 4 years and now wanted to employ the Kelly Betting on it. The problem which arises, is that I need to define a window in which i estimate mean and var of the strategy, thus I would update every day f for the next day using the last n bars. I tried it with n=200, the problem is that f results in beeing very volatile and finally it is a disaster in terms of performance. If estimate f using all the data (1000 bars), then i don't have any data to test forward ..So the big question, how to choose n ? Thanks in advance for your help ...Nicolas

In example 6.2 in your book, you calculate that the compounded return on SPY using the recommended 2.528 Kelly leverage is 13.14%. Do you mean that the return to the investor on the full leveraged amount of $252,800 (using $100,00 in capital), would be 0.1314*252,800 = $33,218 (33% of capital)? Or, do you mean you would get 0.1314*100,000 = $13,140 back using the full $252,800 (using $100,000 in capital)? If you would only get $13,140, this seems very low considering how much money has been borrowed and the 9.8% return without borrowing.

Using the formula in the appendix for example 6.2, I got g = 0.04 + 2.528*(0.1123-0.04) + (.1691*2.528)^2/2 = 31.4%. But what initial investment does gain value this apply to ($100,000 or $252,80?)? Does this mean using $100,000 in capital, and 2.52 leverage I would make $31,400? Essentially, how much money would I have at the end of the year if I had $100,000 in capital, and used the Kelly leverage to borrow up to $252,800 to buy SPY in your example?

What do you recommend in the case of $100k capital trading futures contracts across 3 models (to figure out the # of contracts / model)? One could calculate f for each using e.g. half Kelly and then round down to the get the # contracts / model, however, this does not take into consideration the portfolio risk as a whole...

Grateful for your thoughts and / or links on where to research this.

best regardsEd

PS presumably the formula values of m & r could be either annualized or summed (if longer or shorter period than 1 year) so long as they are both treated equally?

Hi Ed,I am not sure why you think that portfolio risk is not taken into account using Kelly formula. As you can see from example 6.3 of my book, covariance between each futures are used to generate allocation across the 3 contracts.

Means and variances can be of any time frame: Kelly formula is time-scale invariant, unlike the Sharpe ratio.

I'm looking at implementing some of the ideas in your book and have some questions. What I'm specifically looking at is trading a basket of models that all trade different types of futures. The Kelly formula I'm using is to solve the linear system

CF* = M

for F*, the Kelly fraction vector

where C is the covariance of the various models and M is the return vector. All averaged over some recent period.

Now, cranking the numbers I run into two or three practical problems, that mostly boil down to the question "how important are the correlations":

1. I sometimes see negative Kelly fractions for some models, which opens up a few questions..

a. I am assuming I can get negative fractions for even a profitable model because it may be highly correlated with another profitable model but less profitable, so therefore I can use it to hedge.

b. But what do I do with the numbers if I determine that it doesn't in fact make sense to short a model? Maybe I just don't want to do it for some reason (the gains might be small from doing so and it does seem very weird to be shorting a profitable strategy) or maybe you just can't figure out what it "means" to short a complicated model... Specifically if my Kelly fractions are, let's say +2, +2, -1, and I decide I am just going to not trade the -1, what do I do with the other 2s? I am clearly abusing the math since the +2s are calculated under the assumption I'm going to diversify with the -1. Can I just clamp the -1 to 0 and not mess things up?

2. On a similar issue, since these are futures models with different margin requirements, if I have, let's say now +5, +5, +5 that suggests I should put in 15 times my capital, but what if the margin limits stop me from doing so? You suggest that we should then roll back the models equally, to shall we say +2, +2, +2. But if one of the models has much higher margin requirements than the other, it may be that we could instead trade +4, +4, +1.5 ..... again I realize I'm not following what the covariances are telling me, but it seems I should be getting a higher return.

Anon,If you cannot short one of the strategies, why not just exclude it when computing C and M?

If you want to limit the leverage of certain strategies but not others and still want to find the optimal growth rate, you can numerically maximize (with constraints) the compounded growth rate of the portfolio, with the leverage on each strategy as the parameters to be optimized. This constrained optimization can use the backtest returns as input.

(More specifically, you can assume a certain set of leverages for each strategy, compute the compounded growth rate of the entire portfolio using your backtest daily returns, and then vary these leverages while maintaining your constraint until the growth rate is maximized.)

I am doing some calculations similar to the last few posts on this blog i.e. calculating F* for different strategies ... I have quite a lot of them in a large covariance matrix and am looking back around 1/2 year.

One of the things I don't understand (having checked my calculations several times) is that I am getting positive Kelly fractions for some strategies despite them have negative M (geometric returns) over the last 1/2 year or more. I believe that this may be due to the covariance matrix which averages covariances over different periods as a way to deal with strategies that do not trade all the time.

I can understand that the overall equity curve may benefit if these 'recent loser strategies' perform well while others do not. Nonetheless they do make losses.

Can you see a rationale for trading these strategies as described here, or would you exclude any that had negative returns over the calculation window?

thank you

PS calculations that lead to counter-intuitive results can be quite tough to swallow

Anon,Since Kelly formula only cares about overall growth rate, if your losing strategy is anti-correlated with your other strategies, Kelly will recommend that you include it, because it will increase the Sharpe ratio. This is assuming that you can adopt the Kelly optimal leverage. If you can't use that leverage, then it may be optimal to just keep the strategies with the highest returns. I have an example in my new book to illustrate this.Ernie

I have made a few calculations using SPY data from yahoo and I find that the Kelly optimal leverage is about 4.7 as of Friday when I calculate the mean and standard deviation for a 3-year period (750 bars). But when I double the period, the Kelly optimal leverage drops to 0.25. Questions:

(1) Do my calculations make sense to you? I assumed 0 risk free rate

and if they do

(2) how do you explain such huge discrepancy? If optimal Kelly leverage is so sensitive based on period of calculation of statistics then it appears to me that it is rather useless.

Hi Anon,Sure, if you calculate the leverage during a bull market, the leverage should be very high, and if you do so during a bear market, it should be negative!

Kelly leverage can only be applied to a strategy where you believe the return is stable and will persist in that stable value going forward.Applying it to a market index certainly won't yield sensible result unless the period is very long (e.g. 30 years).

Thank you for this post.Do you believe it's wise to calculate two separate Kelly criterions for a long & short model? I'm wondering this since the average long return in my sample model is 0.44% vs. 1.49% for the short part.Best regards,

I just read the Thorp's paper "The Kelly Criterion in Blackjack Sports Betting, and the Stock Market". In example 7.3, Thorp tried to illustrating using fractional kelly criterion. He mentioned that "in the crash of October 1987, the S&P 500 index dropped 23% in a single day. If this happened at leverage of 2.0, the new leverage would suddenly be 77/27=2.85 before readjustment by selling part of the portfolio." I just wonder how to get the ratio "77/27"? Thank you very much.

Suppose the account NAV was $100 before the crash. At a leverage of 2, the market value of the portfolio would be $200. After crash, the market value dropped 23% to $154, and the NAV to 100 + (154-200)=$54. So the new leverage is 154/54=77/27, way higher than the 2 that was prescribed.

Hi Jason,The Kelly formula I described here is based on average return and volatility of returns. It is quite different from the win/loss ratio that is described in your article.

But in any case, applying Kelly to FX is the same as applying to any instrument. We need to calculate the unlevered return and volatility first, and then Kelly formula will tell you what leverage to apply. Then we can be conservative and take a half or even a quarter of that to determine our position size. This "divide by 15 rule" doesn't make any sense.

I see that the link that you provided to Thorp's article is dead. Here's a working link. Your formula is in line 26 on page 24.

This blog entry dates from 2006, before the debacle of 2008. Quite recently I computed the Kelly optimum betting fraction for the S&P500 (more accurately, for SPY with dividends re-invested) for the last 20 years or so of data. My answer is in the last line of the little table on page3 of this tutorial by me. I computed about 2.1 versus your 12.5 . My estimate was based on actual daily return ratios, whereas Thorp's approach utilized the central limit theorem's conclusion to the effect that the final distribution would be log-normal.

However I'm supposing that the main difference is the 2008 debacle, which was beyond your posting date but within the period of record that I used. On page2 of that same tutorial by me you'll find another table, below a chart. And on the last line of it in the 1-year column I have the geometric version of the Sharpe ratio for buy-and-hold SPY. For a 1-year duration there is essentially no quantitative difference between my geometric version of the Sharpe ratio and the original arithmetic version.

My chart and table were designed to illustrate the fact there are innate costs of stop-loss trading, and in an entertaining and interactive way--- not particularly to compute the Sharpe ratio. But if you set the Number of Samples in the upper right-hand corner to 500 (to minimize the noise) and click numerous times on Resample you'll get a fair idea of the Sharpe ratio over the entire two-decade period. It's something like 0.3 versus your 1.0 .

So that plus the extreme volatility of the 2008 debacle, which happened after you did your rough estimate, could account for the difference in f*s.

About fixed-fractional betting requiring us to sell on the way down, that's certainly correct for the case that you present, of using a 6+ leveraging factor. However if the assumed betting fraction is less than 1.0 you buy on the way down--- the opposite of what you do if you're leveraged. This is carefully explained and derived about half-way down on the right hand side of page3 of that particular tutorial by me. You still can't go broke except possibly during one-day crashes during which you are not able to trade.

On another page of that site I have a more complete discussion of the Kelly mathematics and an entertaining Poundstone Wheel of Fortune Javascript widget (done with the kind permission of Mr. Poundstone).

In general my discussions are highly dismissive of the advisability of using leverage. I should add that in some places I simplified the mathematics, the given equations, by assuming in effect that the interest on cash was zero. However, where it mattered, such as on the graphics and tables for the S&P500 I fully took interest rates into account--- it's in the JavaScript for your inspection.

Ernie Chan I'm very pleased to have discovered your blog and will be staying tuned. I will soon have a big and meaty article up on hypothesis testing, the null hypothesis, etc. with real-data illustrations based on the concept of momentum.

I'm much appreciate your comments and corrections to this and regarding everything on my site (on which an email link is provided at the end of each article).

Hi Mike,Yes, I agree that the optimal Kelly leverage formula computed by Thorp (m/s^2) has one major limitation: it assumes Gaussian distribution of returns. Clearly, the 2008 crisis has once again shown us that returns are not Gaussian! Hence I am very wary of using Kelly leverage, even half-Kelly leverage. I am now much more dependent on the historical worst case scenario when setting my leverage. However, the Kelly formula's prescription of fixing the leverage, thus decreasing position size when we lose, is still a good one. (It is interesting that you point out that the opposite happens if our leverage is < 1!)

Thanks for introducing your website to us. I will be checking it out going forward.

For example, for simple buy and hold strategy for portfolio of ES, CL, GC (daily retruns) I get following F-vector for some period of time:

1.2747 -1.5736 4.8090

Does this mean I have to trade roughly 1 contract ES, -1 contract CL and 5 contracts GC?

How do i go about the inherent leverage of futures markets where for example for ES I need only to provide 5000 of the initial margin? So when I buy ES at 2000, I effectively use 2000*50$/5000 = leverage of 20! and Kelly formula tells me to use leverage of 1.27. Does this mean i need to provide at least 2000*50$/1.27 = 78750 of equity to trade 1 contract? How do i go about portfolio risk management if my account is less thatn this amount?

Hi P_Ser,No, those numbers refer not to number of contracts, but to the ratio between the market value of a contract to the account equity.

So if you have $1 equity, you should buy $1.27 of ES, short $1.57 of CL, etc.

Kelly leverage does not care about the margin requirement. If the Kelly leverage is higher than the maximum leverage (minimum margin) allowed by the exchange or your broker, then you have to run a constrained maximization program instead. See http://epchan.blogspot.ca/2014/08/kelly-vs-markowitz-portfolio.html. Alternatively, you can just divide all the leverages by some fudge factor so that all of them are lower than the maximum allowed.

Hi,I also got confused. The book is not precise in this matter. I think that allocation is not the same as leverage and you cannot use full leverage recomended by Kelly formula to buy single stock/contract

Hi Mario,Allocation refers to the individual leverages applied to each and every instrument in a portfolio. The sum of these individual leverages is the overall leverage applied to the portfolio.

For e.g. if a portfolio consists of AAPL, GOOG, and MSFT, with allocation (1.5, 4, 2), that means for $1 account equity, we should buy $1.5 AAPL, $4 GOOG, and $2 MSFT. The overall leverage is 1.5+4+2=7.5.

Kelly formula will determine the allocation, and therefore the overall leverage of the portfolio.

Let's assume that we are trading 1 strategy but on 100 stocks.Today we receive signal for AAPL. Should I buy AAPL stocks for 7.5 * capital?What if during this trade I receive next signal (e.g. tomorrow) for GOOG - then I should reduce AAPL and buy GOOG (here comes proper allocation) but in total, I will be invested at 7.5 * capital all the time?

If you are applying Kelly leverage to a strategy, and not to individual stocks in a static portfolio, then the leverage is to be interpreted as the maximum leverage of the positions that the strategy may hold at any one time.

So if your strategy may long AAPL, GOOG, MSFT at the same time, but today it only has buy signal for AAPL, you *may* buy $7.5 of AAPL, or you may buy less. This allocation is determined by your strategy, not by Kelly leverage. Kelly leverage will only tell you what the maximum leverage your strategy should hold, since the input to the Kelly formula is the strategy returns, not the returns of individual positions held by your strategy.

I recently bought and am currently reading one of Ernie's books (Quantitative Trading - How to Build your Own Algorithmic Trading Business). First, let me say that it is nicely written and quite approachable for a rookie like me. And perhaps my rookie status may explain the questions I have for the readers here. I am backtesting a strategy and obtained for it a portfolio Sharpe Ration of 0.1, which I know is bad. But when I compute the Kelly leverage vector for the portfolio, assuming I have a retail brokerage account, I am getting that all my Kelly leverage factors are less than 1. I am having trouble understanding what it means to have a Kelly leverage of less than 1 and whether that can even be a realistic condition in a profitable strategy. In other words, what if some of my leverage factors were greater than 1 and some were less than 1? Could someone please provide some qualitative insights on this? Thank you in advance and kind regards. Marc

Marc,Given that your Sharpe ratio is 0.1, it isn't surprising that the optimal Kelly leverage is less than 1. It has nothing to do with the leverage constraint in your brokerage account. It just means you should only trade a small size (< account NAV) for your strategy, since it isn't very consistently profitable. Ernie

Many thanks for taking the time to provide an answer to my questions. I understand perfectly now. My misunderstanding was that the Kelly formula could only tell you how much leverage to get, but I understand now that it can also tell you by how much you should scale back a trade.

I currently run a fund with about 30 strategies. Some of the strategies trigger infrequently and some are the same strategies with different parameters. I'm trying to use the Kelly Criterion System to allocate capital but my covariance matrix is an issue.

a. How do I handle infrequent strategies that result in "nan" entries in my covariance matrix? If, for conservatism, I assume these strategies are perfectly correlated my covariance matrix becomes singular.

b. Because some strategies simply trade the same alpha (but along different parameters), these strategies have very high correlations. This results in my covariance matrix being singular and intractable. Should I rather trade one algo to avoid the correlation? Should I assume a correlation of maybe 0.5? How do I deal with this issue?

Hi,1) A strategy that doesn't trade during a certain period has a return of 0, not NaN. So the covariance matrix should not contain NaN's. If 2 strategies never trade during same period, this will cause Kelly to allocate to them almost like two statistically independent strategies.2) If two variations of the same strategy are traded, you should just allocate equally to them, and just use their average returns to represent this strategy in covariance calculations.Ernie